Francesco Lapi1, Elisa Bianchini2, Iacopo Cricelli2, Gianluca Trifirò3, Giampiero Mazzaglia2, Claudio Cricelli4. 1. Health Search, Italian College of General Practitioners and Primary Care, Florence, Italy. Electronic address: lapi.francesco@simg.it. 2. Health Search, Italian College of General Practitioners and Primary Care, Florence, Italy. 3. Section of Pharmacology, Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy. 4. Italian College of General Practitioners and Primary Care, Florence, Italy.
Abstract
OBJECTIVE: To develop and validate the Italian Health Search Morbidity (HSM) Index to adjust health care costs in general practice. METHODS: The study population comprised 1,076,311 patients registered in the Health Search CSD Longitudinal Patient Database between January 1, 2008, and December 31, 2010. We randomly selected 538,254 and 538,057 patients to form the development and validation cohorts, respectively. To ensure model convergence, 5% of the aforementioned cohorts were selected randomly to create development and validation samples. The outcome was the total direct health care costs covered by the national health system. Interaction between age and sex, chronic diseases, and acute diseases were entered in a multilevel generalized linear latent mixed model with random intercepts (province of residence and general practitioner) to identify determinants associated with increased or decreased costs. The estimated coefficients were linearly combined to create the HSM Index for individual patients. The score was applied to the validation sample, and measures of predictive accuracy, explained variance, and the observed/predicted ratio were computed to evaluate the model's accuracy. RESULTS: The mean yearly cost was €414.57 per patient, and the HSM Index had a median value of 5.08 (25th-75th range 4.44-5.98). The HSM Index explained 50.17% of the variation in costs. Concerning calibration, in 80% of the population, the margin of error in the estimation of costs was around 10%. CONCLUSIONS: The HSM Index is a reliable case-mix system that could be implemented in general practice for costs adjustment. This tool should ensure fairer scrutiny of resource use and allocation of budgets among general practitioners.
OBJECTIVE: To develop and validate the Italian Health Search Morbidity (HSM) Index to adjust health care costs in general practice. METHODS: The study population comprised 1,076,311 patients registered in the Health Search CSD LongitudinalPatient Database between January 1, 2008, and December 31, 2010. We randomly selected 538,254 and 538,057 patients to form the development and validation cohorts, respectively. To ensure model convergence, 5% of the aforementioned cohorts were selected randomly to create development and validation samples. The outcome was the total direct health care costs covered by the national health system. Interaction between age and sex, chronic diseases, and acute diseases were entered in a multilevel generalized linear latent mixed model with random intercepts (province of residence and general practitioner) to identify determinants associated with increased or decreased costs. The estimated coefficients were linearly combined to create the HSM Index for individual patients. The score was applied to the validation sample, and measures of predictive accuracy, explained variance, and the observed/predicted ratio were computed to evaluate the model's accuracy. RESULTS: The mean yearly cost was €414.57 per patient, and the HSM Index had a median value of 5.08 (25th-75th range 4.44-5.98). The HSM Index explained 50.17% of the variation in costs. Concerning calibration, in 80% of the population, the margin of error in the estimation of costs was around 10%. CONCLUSIONS: The HSM Index is a reliable case-mix system that could be implemented in general practice for costs adjustment. This tool should ensure fairer scrutiny of resource use and allocation of budgets among general practitioners.
Authors: Eng Sing Lee; Hui Li Koh; Elaine Qiao-Ying Ho; Sok Huang Teo; Fang Yan Wong; Bridget L Ryan; Martin Fortin; Moira Stewart Journal: BMJ Open Date: 2021-05-05 Impact factor: 2.692